1. Field of the Invention
The present invention relates to power generation and management, and more particularly, to predictive-modeling optimization of microgrid distributed power generation and management using emergency power generation equipment.
2. Description of the Prior Art
Prior art provides for power generation, generally for methods for optimizing microgrid function based on predicted or forecasted demand, including systems and methods for optimizing microgrid distributed power usage based on predictive algorithms of power demand.
By way of example the following are relevant prior art documents relating to power management:
U.S. Pat. No. 7,115,010 and U.S. Publication 2004/0051387 for “Control of small distributed energy resources, assigned to Wisconsin Alumni Research Foundation”, which describe and teach a microsource system for providing power in an isolation mode or in a grid mode that is configured to couple to a power system without modification of the existing equipment in the power system, wherein the microsource system is configured for use in a microgrid, and wherein the microsource power source may be a fuel cell, a microturbine, battery, or photovoltaic cell.
U.S. Pat. No. 7,983,799 and U.S. Publication 2011/0118885 for “System and method for controlling microgrid”, assigned on the document faces to General Electric, which disclose and teach a system for controlling a microgrid including microgrid assets, with at least one of the microgrid assets comprising a different type of electrical generator than an electrical generator of another of the microgrid assets; a tieline for coupling the microgrid to a bulk grid; and a tieline controller for providing tieline control signals to adjust active and reactive power in microgrid assets, and further describes that the electrical generators comprise at least one renewable energy source.
U.S. Pat. No. 7,834,479 and U.S. Publication 2008/0278000 for “Methods and systems for intentionally isolating distributed power generation sources”, assigned on the document faces to Beacon Power Corporation, which disclose and teach a method for operating a mini-grid including one or more power generation sources and one or more loads connected to a bus. The method includes the steps of: monitoring a condition of the utility grid; disconnecting the mini-grid from the utility grid to operate the mini-grid independently in response to a power disruption over the utility grid; monitoring at least one of a frequency and a voltage of power on the bus; and providing an interconnect device connected to the bus, the interconnect device including at least one of: an energy storage device for absorbing or releasing real power to control the frequency of the power on the bus, and power quality compensator for absorbing or releasing reactive power to control the voltage of the power on the bus.
U.S. Publication 2007/0040382 for “Self-supporting power generation station”, by inventor Towada, which teaches a scalable microgrid for providing power to areas remote from the existing power grid, wherein the microgrid comprises at least two power pods linked in parallel, and each power pod has at least one micro-turbine fueled by methane gas, and wherein additional power pods may be added as power needs increase.
By way of example, relevant documents relating to power management and optimization include:
U.S. Publication 2009/0062969 for “Hybrid robust predictive optimization method of power system dispatch”, assigned on the document to General Electric, which describes a system for controlling and optimizing operation of a microgrid by integrating power generation, load and storage assets; it also describes a predictive algorithm that is used to dynamically schedule different assets, the predictive algorithm optimizes the microgrid operation over a predetermined time horizon based on predicted future microgrid asset conditions.
U.S. Publications 2010/0179704 and 2011/0035073 for “Optimization of microgrid energy use and distribution”, assigned on the document face to Integral Analytics, Inc., which describes a system for optimization of energy use and distribution within a microgrid system, including forecasting of individualized demand by end-use or individualized demand by location for at least one customer or customer location, wherein forecasting of individualized demand may include inputs including: load prediction, weather forecast, risk given load uncertainty; customer compliance forecasts, customer probability of override forecasts; time of day effects; and day of week effects.
U.S. Publication 2010/0222934 for “System for managing energy at loads”, by inventors Iino, et al., which teaches an energy management system comprising a demand prediction unit configured to predict demand at a load to which energy is supplied and a load adjustment range prediction unit to predict a load adjustment range by using historical data, wherein the system is applied to a microgrid capable of performing demand-side management.
U.S. Publication 2011/0082596 for “Real time microgrid power analytics portal for mission critical power systems” and U.S. Publication 2011/0082597 for “Microgrid model based automated real time simulation for market based electric power system optimization”, each assigned on the document face to EDSA Micro Corporation, which describe a system for real-time modeling of electrical system performance of a microgrid electrical system, wherein predicted data for the electrical system is generating using a virtual system model, and the virtual system model is updated based on real-time data to forecast the cost of operating the microgrid and the reliability and availability of the microgrid system. Furthermore, in relevant art, it is known to describe how energy pricing is integrated into the described forecasting models. By way of example of relevant prior art documents, consider the following:
U.S. Publication 2011/0082596 for “Real time microgrid power analytics portal for mission critical power systems” and U.S. Publication 2011/0082597 for “Microgrid model based automated real time simulation for market based electric power system optimization”, each assigned on the document faces to EDSA Micro Corporation, which teach a system for real-time modeling of electrical system performance of a microgrid electrical system, wherein predicted data for the electrical system is generating using a virtual system model that is updated based on real-time data to forecast the cost of operating the microgrid and the reliability and availability of the microgrid system. Furthermore, all transactions between the public electric service on the macrogrid and the microgrid infrastructure are closely monitored, and rate and pricing information for the management of electricity exchange are also maintained. Closely monitoring this information and updating the virtual and real time models accordingly allows the systems and methods disclosed herein to optimize energy consumption to meet various objectives of the microgrid operator, wherein predicted data can be used to generate market-based pricing predictions based on the performance of the components of the electrical system.
U.S. Publication 2008/0262820 for “Real-time predictive systems for intelligent energy monitoring and management of electrical power networks” and U.S. Publication 2009/0063122 for “Real-time stability indexing for intelligent energy monitoring and management of electrical power network system”, each assigned to EDSA Micro Corporation, which teach the following: the '820 publication describes a system for intelligent monitoring and management of an electrical system including a data acquisition component to acquire real-time data from the electrical system; a power analytics server comprising a real-time energy pricing engine connected to a utility power pricing data table and configured to generate real-time utility power pricing data, a virtual system modeling engine to generate predicted data output for the electrical system, an analytics engine configured to monitor the real-time data output and the predicted data output of the electrical system, a machine learning engine configured to store and process patterns observed from the real-time data output and the predicted data output and configured to forecast an aspect of the electrical system. The '122 publication is a continuation-in-part of '820 and also describes a system for intelligent monitoring and management of an electrical system
U.S. Publication 2010/0198421 for “Methods and apparatus for design and control of multi-port power electronic interface for renewable energy sources”, assigned on the document face to Board of Regents, The University of Texas System, which teaches a method for managing energy movement wherein a determination of whether operational characteristics should be modified is based on at least one factor of: a renewable energy generation forecast, an energy consumption forecast, and a substantially real-time price of energy, with the application of this method and apparatus in a microgrid setting.
U.S. Pat. No. 7,873,442 and U.S. Publication 2006/0206240 for “System and method for managing and optimizing power use”, each assigned on the respective document faces to The Energy Authority, Inc., which describe an optimization method for the use of utility power including the steps of: initializing a utility power load requirement forecast, an amount of available utility power, and aggressiveness position for optimizing the use of available power, a utility power schedule; determining an initial power use position for a peak load utility power use range and a low load range; adjusting the utility power use for real-time transactions, adjusting for utility power storage flexibility, and producing a utility power use schedule optimized for use of said utility power in low load range and peak load range, wherein the real-time schedule optimization provides information on how to adjust the use of resources when updated load forecasts based on actual load, and market prices change during the day.
U.S. Pat. No. 7,930,070 and U.S. Publication 2010/0076613 for “System, method, and module capable of curtailing energy production within congestive grid operating environments”, and U.S. Publication 2011/0172835 for “System and method of curtailing energy production within congestive grid operating environments”, each assigned on the document face to Kingston Consulting, Inc., which describe a method of managing power generation that provides a framework to allow proactive management of alternative energy production through asset monitoring and characterization relative to real-time and anticipated grid conditions, and further describes that the energy management system can perform congestion forecasting, energy output forecasting, proactive curtailments, storage control, dispatch control, real-time pricing, dynamic pricing, or various combinations of features, and a remote monitor and control module that can include on-grid and off-grid control logic, real-time performance monitoring, meteorological data interface, microgrid or asynchronous transmission capabilities, local performance characterization logic, a control panel, or various combinations of features.
U.S. Publication 2011/0093127 for “Distributed energy resources manager” by inventor Kaplan, which describes a distributed energy resources manager that connects electrical assets in an electricity distribution grid with other information processing systems to optimize a flow of electric power within the electricity distribution grid.
Further describes that distributed resources may be utilized to meet system-wide needs such as reducing peak consumption, storing excess utility-scale wind or solar power, responding to price signals including real-time or critical peak pricing, or supply ancillary grid services.
U.S. Publication 2011/0071882 for “Method and system for intermediate to long- term forecasting of electric prices and energy demand for integrated supply-side energy planning”, assigned on the document face to International Business Machines Corporation, which describes a method of price forecasting in an electrical energy supply network and/or load (energy demand) forecasting of a given consumer of electrical energy, for identifying the optimal mix of energy hedge and exposure to day ahead/spot market prices for deriving economic benefits in overall energy expenditure; and further describes modeling using real time price and day ahead price data and probability distributions.
U.S. Pat. No. 7,657,480 for “Decision support system and method”, which was assigned on the document face to Air Liquide Large Industries, and describes a computer-implemented method for identifying an excess energy capacity in a production supply chain by a supply chain operator, in which the supply chain operator also operates at least one power generation facility to sustain industrial production by the production supply chain, the supply chain operator is capable of consuming and selling electricity produced by the power generation facility; and further describes that the forecasted price for electricity during a time period is determined by a forecasting and planning model utilizing historical and real-time data, including the real-time commodity prices for electricity.
U.S. Pat. No. 6,583,521 for “Energy management system which includes on-site energy supply” to inventors Lagod, et al., which describes a system for managing the supply of power to a load that receives power from an electric grid, including: at least one on-site power generator that is capable of supplying power to the load independently of the power grid; a controller which processes data relating to at least one factor that is predictive of the reliability and/or quality of power supplied to the load, and selects the power grid or the on-site generator as a preferred power source; and a switch which is responsive to the selection of the preferred power source to connect the load to the selected power source, and further describes that the selection of the preferred power source may be on the basis of relative costs of power supplied via the power grid and the on-site generator; and the relative costs may include data regarding operating costs of the on-site generator, the price of fuel consumed by the on-site generator, and time-of-day pricing (including real time pricing) of power supplied via the power grid.
U.S. Publication 2005/0015283 for“Electric-power-generating-facility operation management support system, electric-power-generating-facility operation management support method, and program for executing support method, and program for executing operation management support method on computer”, assigned on the document face to Kabushiki Kaisha Toshiba, which describes an electric-power-generating-facility operation management support system for determining economically-optimal operational conditions based upon real-time information with regard to the demand for the electric power and the price thereof as well as information with regard to properties of the electric power generating facilities.